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September 7, 2021

The lab ecosystem and its myriad intricacies are often not discussed in the business world. Managing all aspects of a lab as an integrated ecosystem is indeed challenging for businesses, especially because R&D is often considered a black box with unquantifiable returns on investment. A common question that enterprises from the specialty chemicals, coatings, or food ingredient industries ask is how they can better use the vast volumes of laboratory data they generate.

How can such information drive better decisions and what approach must firms take to address the challenges of lab management? We explore these questions and the common challenges faced by chemical companies through a neural approach.

Present-day challenges of chemical firms 

Generally, labs serve two key functions – one where a significant part of the research and development happens, and the second is where the sample is tested for quality assurance during production. This often limits the collaboration between research and manufacturing teams. Today, a researcher in a chemical R&D lab has to sift through a large amount of data and reports and deals with various constraints and limitations of testing in physical labs. 

With the proliferation of digital tools and techniques, data management is becoming increasingly important. Typically, labs contain various instruments, lab management software that interface with enterprise systems, and an inventory of lab supplies. The diverse base of instruments also contributes to the vast amounts of data generated in a lab, and with data availability limited to instruments, the value of data is not fully unlocked. Finding the right data manually can be time consuming and accessing quality data for informed decision-making is always a challenge. These reasons make data management in labs a complex and laborious task.  

On the other hand, the ever-augmenting portfolio of applications utilized in labs is making the technology landscape more complex. The lack of standardization in instrument onboarding and integration, inadequate management of lab supplies, and absence of instrument availability add to the challenge of managing instruments and interfaces, making lab management more cumbersome and inefficient. 

The neural approach 

To better manage their data, chemical companies can adopt a neural system to consolidate information from disparate labs, provide intelligent insights on products, and ensure collaboration with partner labs and ecosystem players. Such a system forms the core of Neural ManufacturingTM, a thought leadership framework  that propounds having an intensely networked set of partners aligned to a common purpose. The value chains of a neural system are responsive, adaptive, and personalized with intelligence built on the edge of the networks and rely on automation and machine-first delivery models. 

So, how can chemical companies leverage neural behaviors and traits to drive operational agility in the labs of the future?

  • Connectedness: An enterprise portal or an asset performance management based approach for equipment onboarding, coupled with an internet of things (IoT) platform, will provide end-to-end connctivity for a lab. This will pave the way for creating a centralized data repository or a data lake. Data harmonization will ensure that data is available wherever and whenever required for informed decision-making.

  • Intelligent and cognitive: Once data is available, artificial intelligence and machine learning-based (AI-ML) models can be leveraged to extract key insights and identify potential improvement opportunities. Instrument data can be used for predictive diagnostics to keep a check on maintenance activities and possible alternatives. With an IoT platform in place, lab activities can be monitored remotely without any interruptions. Additionally, reporting dashboards can be created to provide visibility into lab operations. This will ultimately lead to a data-driven approach for managing labs.

  • Automation: By leveraging cobots, complex and hazardous experiments can be automated. Electronic lab notebooks (ELN) can digitalize and automate the data gathering process and help researchers move towards a paperless lab environment. Augmented reality can help in guided experimentation, smart chatbots can provide support for natural language-based queries, and virtual reality can support in assisted training and troubleshooting.

Final thoughts 

Although we see chemical companies leveraging digital technologies across the value chain, from procurement to after-sales, there is a need to bring R&D into the fold as well. By adopting neural traits, the R&D function can be transformed from a siloed black box entity to a digitalized and integrated system to calculate and realize returns better. Adopting a use case driven approach to identify and transform operations one step at a time, can help chemical organizations and their stakeholders in their transformation journey.

Rahil Mylapally is a business analyst with TCS’ Manufacturing business unit. He has over seven years of experience in providing business and IT consulting services for Fortune 500 customers. His areas of expertise include business and industry analysis, process transformation, supply chain management, and data analytics. Rahul holds a Master’s degree in supply chain management from the National Institute of Industrial Engineering (NITIE), Mumbai, India.

Prudhvi Kumar V is an assistant consultant with TCS’ Manufacturing business unit. He has over seven years of experience working with TCS’ customers in designing transformative solutions for supply chain management. He has worked on multiple projects for global enterprises from both discrete and continuous manufacturing. He holds a Master’s degree in supply chain and operations from NITIE, Mumbai, India and a Bachelor’s degree in mechanical engineering from the NIT, Jalandhar, India.

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